{"id":75,"date":"2018-05-16T14:13:29","date_gmt":"2018-05-16T14:13:29","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/suny-oldwestbury-sociological-inquiry\/chapter\/sampling\/"},"modified":"2018-05-16T20:10:22","modified_gmt":"2018-05-16T20:10:22","slug":"sampling","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/suny-oldwestbury-sociological-inquiry\/chapter\/sampling\/","title":{"raw":"Chapter 7: Sampling","rendered":"Chapter 7: Sampling"},"content":{"raw":"
\r\n
<\/div>\r\n
\r\n\r\n \r\n\r\n<\/div>\r\n<\/div>\r\n
\r\n
\r\n

Chapter 7<\/span> Sampling<\/h1>\r\n
\r\n

Who or What?<\/h2>\r\n

Remember back in Chapter 1 \"Introduction\"<\/a> when we saw the cute photo of the babies hanging out together and one of them was wearing a green onesie? I mentioned there that if we were to conclude that all babies wore green based on the photo that we would have committed selective observation. In that example of informal observation, our sampling strategy (just observing the baby in green) was of course faulty, but we nevertheless would have engaged in sampling. Sampling has to do with selecting some subset of one\u2019s group of interest (in this case, babies) and drawing conclusions from that subset. How we sample and who we sample shapes what sorts of conclusions we are able to draw. Ultimately, this chapter focuses on questions about the who or the what that you want to be able to make claims about in your research. In the following sections we\u2019ll define sampling, discuss different types of sampling strategies, and consider how to judge the quality of samples as consumers of social scientific research.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

\r\n

7.1<\/span> Populations Versus Samples<\/h2>\r\n
\r\n

Learning Objective<\/h3>\r\n
    \r\n \t
  1. Understand the difference between populations and samples.<\/li>\r\n<\/ol>\r\n<\/div>\r\n

    When I teach research methods, my students are sometimes disheartened to discover that the research projects they complete during the course will not make it possible for them to make sweeping claims about \u201call\u201d of whomever it is that they\u2019re interested in studying. What they fail to realize, however, is that they are not alone. One of the most surprising and frustrating lessons research methods students learn is that there is a difference between one\u2019s population of interest and one\u2019s study sample. While there are certainly exceptions, more often than not a researcher\u2019s population and her or his sample are not the same.<\/p>\r\n

    In social scientific research, a population<\/a>The group (be it people, events, etc.) that you want to be able to draw conclusions about at the end of your study.<\/span><\/span> is the cluster of people, events, things, or other phenomena that you are most interested in; it is often the \u201cwho\u201d or \u201cwhat\u201d that you want to be able to say something about at the end of your study. Populations in research may be rather large, such as \u201cthe American people,\u201d but they are more typically a little less vague than that. For example, a large study for which the population of interest really is the American people will likely specify which American people, such as adults over the age of 18 or citizens or legal residents. A sample<\/a>The group (be it people, events, etc.) from which you actually collect data.<\/span><\/span>, on the other hand, is the cluster of people or events, for example, from or about which you will actually gather data. Some sampling strategies allow researchers to make claims about populations that are much larger than their actually sample with a fair amount of confidence. Other sampling strategies are designed to allow researchers to make theoretical contributions rather than to make sweeping claims about large populations. We\u2019ll discuss both types of strategies later in this chapter.<\/p>\r\n

    As I\u2019ve now said a couple of times, it is quite rare for a researcher to gather data from their entire population of interest. This might sound surprising or disappointing until you think about the kinds of research questions that sociologists typically ask. For example, let\u2019s say we wish to answer the following research question: \u201cHow do men\u2019s and women\u2019s college experiences differ, and how are they similar?\u201d Would you expect to be able to collect data from all college students across all nations from all historical time periods? Unless you plan to make answering this research question your entire life\u2019s work (and then some), I\u2019m guessing your answer is a resounding no way. So what to do? Does not having the time or resources to gather data from every single person of interest mean having to give up your research interest? Absolutely not. It just means having to make some hard choices about sampling, and then being honest with yourself and your readers about the limitations of your study based on the sample from whom you were able to actually collect data.<\/p>\r\n

    Sampling<\/a>The process of selecting observations that will be analyzed for research purposes.<\/span><\/span> is the process of selecting observations that will be analyzed for research purposes. Both qualitative and quantitative researchers use sampling techniques to help them identify the what or whom from which they will collect their observations. Because the goals of qualitative and quantitative research differ, however, so, too, do the sampling procedures of the researchers employing these methods. First, we examine sampling types and techniques used in qualitative research. After that, we\u2019ll look at how sampling typically works in quantitative research.<\/p>\r\n\r\n

    \r\n

    Key Takeaways<\/h3>\r\n